At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. David was a Fellow in our Winter 2016 cohort who landed a job with one of our hiring partners, Amazon.

Tell us about your background. How did it set you up to be a great data scientist?

Before joining The Data Incubator, I completed my Ph.D. in chemistry at Johns Hopkins University, where I focused on the design and synthesis of new magnetic materials. My work gave me the opportunity to work alongside scientists in many different disciplines, and exposed me to a vast array of experimental techniques and theoretical constructs. From a data science perspective, this meant that I was constantly encountering new types of data and searching for scientifically rigorous models to explain those results. As the volume and complexity of these datasets increased, graphical data analysis tools like Excel and Origin weren’t making the cut for me, and I gradually made the transition to performing data transformation and analysis entirely in Python. That was a big technical leap that took a lot of time and frustration, but I think it ultimately made me a better researcher.From a research perspective, working in a vibrant academic setting also meant learning how to ask bold questions, even at the risk of sounding stupid in front of a large group of mentors and peers–something I’ve done more than I care to admit. For me, finding the right question to ask is just as important as having the technical expertise to find an answer, and that’s one of the things that makes Data Science so exciting.